Proceedings of the 10th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data最新文献

AtlasHDF: an efficient big data framework for GeoAI AtlasHDF: GeoAI的高效大数据框架

Proceedings of the 10th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data Pub Date : 2022-11-01 DOI: 10.1145/3557917.3567615

M. Werner, Haomin Li

{"title":"AtlasHDF: an efficient big data framework for GeoAI","authors":"M. Werner, Haomin Li","doi":"10.1145/3557917.3567615","DOIUrl":"https://doi.org/10.1145/3557917.3567615","url":null,"abstract":"The last decade witnesses a fast development in geospatial application of artificial intelligence (GeoAI). However, due to the misalignment with wider computer science progresses, the geospatial community, for a long time, keeps working with powerful and over-sophisticated tools and software, whose functionality goes far beyond the actual basic need of GeoAI tasks. This fact, to a certain extent, hinders our steps towards establishing future sustainable and replicable GeoAI models. In this paper, we aim to address this challenge by introducing an efficient big data framework based on the modern HDF5 technology, called AtlasHDF, in which we designed lossless data mappings (immediate mapping and analysis-ready mapping) from OpenStreetMap (OSM) vector data into a single HDF5 data container to facilitate fast and flexible GeoAI applications learnt from OSM data. Since the HDF5 is included as a default dependency in most GeoAI and high performance computing (HPC) environments, the proposed AtlasHDF provides a cross-platformm and single-techonology solution of handling heterogeneous big geodata for GeoAI. As a case study, we conducted a comparative analysis of the AtlasHDF framework with three commonly-used data formats (i.e., PBF, Shapefile and GeoPackage) using the latest OSM data from the city of Berlin (Germany), then elaborated on the advantages of each data format w.r.t file size, querying, rending, dependency, data extendability. Given a wide range of GeoAI tasks that can potentially benefit from our framework, our future work will focus on extending the framework to heterogeneous big geodata (vector and raster) to support seamless and fast data integration without any geospatial software dependency until the training stage of GeoAI. A reference implementation of the framework developed in this paper is provided to the public at: https://github.com/tumbgd/hdf4water.","PeriodicalId":152788,"journal":{"name":"Proceedings of the 10th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132380253","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

GEAR 齿轮

Proceedings of the 10th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data Pub Date : 2022-11-01 DOI: 10.1145/3557917.3567616

Yuhei Senuma, Zhao Wang, Yuusuke Nakano, Jun Ohya

{"title":"GEAR","authors":"Yuhei Senuma, Zhao Wang, Yuusuke Nakano, Jun Ohya","doi":"10.1145/3557917.3567616","DOIUrl":"https://doi.org/10.1145/3557917.3567616","url":null,"abstract":"In combinatorial optimization problems, the traveling salesman problem (TSP) and the vehicle routing problem (VRP) have been widely used in many business scenarios such as transportation management and disaster response. However, conventional methods for these problems still have potential for improvement in terms of accuracy and scalability. Meanwhile, in practical business applications, issues such as routing and re-planning while considering real road conditions remain unsolved. In this paper, we propose a Graph Edge Attention Routing (GEAR) model that solves the above-mentioned problems in both academic research and real-world applications. Specifically, GEAR embeds the features of nodes (destinations to be visited) and edges (moving costs between each two nodes) through Graph Convolutional Networks (GCN). We then combine GCN features and Pointer Networks [1] to construct the policy network model of the GEAR and train it by minimizing the total edge cost through REINFORCE with baseline. Experimental results show that GEAR outperforms the conventional methods in both standard and large-scale combinatorial optimization problems. Additionally, GEAR also enables routes to be re-planned when impassable sections of roads appear, which commonly occurs in real-world business scenarios. Furthermore, by testing GEAR on an enterprise-level business dataset by using the commercial Map API to extract real distances between nodes, GEAR can achieve tour efficiency without real data being used to train it and proves its effectiveness and robustness in real-world combinatorial optimization applications.","PeriodicalId":152788,"journal":{"name":"Proceedings of the 10th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121185678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

CSCD

Proceedings of the 10th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data Pub Date : 2022-11-01 DOI: 10.1145/3557917.3567619

Yan Li, Majid Farhadloo, S. Krishnan, Yiqun Xie, T. Frankel, Shashi Shekhar, A. Rao

{"title":"CSCD","authors":"Yan Li, Majid Farhadloo, S. Krishnan, Yiqun Xie, T. Frankel, Shashi Shekhar, A. Rao","doi":"10.1145/3557917.3567619","DOIUrl":"https://doi.org/10.1145/3557917.3567619","url":null,"abstract":"Contrasting spatial co-location pattern discovery aims to find subsets of spatial features whose prevalences are substantially different in two spatial domains. This problem is important for generating hypotheses in many spatial applications, including oncology, regional economics, ecology, and epidemiology. In oncology, for example, this problem is important in developing immune-checkpoint inhibitor therapy for cancer treatment. This problem is challenging due to the large number of potential patterns that are exponentially related to the number of input spatial features. Traditional methods of co-location pattern detection require multiple runs, making computationally expensive and do not scale to large datasets. To address these limitations, we propose a Contrasting Spatial Co-location Discovery (CSCD) framework and contribute two filter-refine algorithms that exploit a novel interest measure; the participation index distribution difference (PIDD). Experiments on multiple cancer datasets (e.g., MxIF) show that the proposed algorithm yields substantial computational time savings compared with a baseline algorithm. A real-world case study demonstrates that the proposed work discovers patterns that are missed by the related work and have the potential to inspire new scientific discovery.","PeriodicalId":152788,"journal":{"name":"Proceedings of the 10th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114363703","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

RING-Net: road inference from GPS trajectories using a deep segmentation network 环网:道路推断从GPS轨迹使用深度分割网络

Proceedings of the 10th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data Pub Date : 2022-11-01 DOI: 10.1145/3557917.3567617

E. Eftelioglu, Ravi Garg, Vaibhav Kango, Chintan Gohil, Amber Roy Chowdhury

{"title":"RING-Net: road inference from GPS trajectories using a deep segmentation network","authors":"E. Eftelioglu, Ravi Garg, Vaibhav Kango, Chintan Gohil, Amber Roy Chowdhury","doi":"10.1145/3557917.3567617","DOIUrl":"https://doi.org/10.1145/3557917.3567617","url":null,"abstract":"Accurate and rich representation of roads in a map is critical for safe and efficient navigation experience. Often, open source road data is incomplete and manually adding roads is labor intensive and consequently expensive. In this paper, we propose RING-Net, an approach for Road INference from GPS trajectories using a deep image segmentation Network. Previous work on road inference is either focused on satellite images or GPS trajectories, but they are not compatible with each other when there is a lack of high quality data from either of the source types. Even though it is primarily focused on using GPS trajectories as its input, RING-Net architecture is flexible enough to be used with multiple data sources with minimal effort. More specifically, RING-Net converts raw GPS trajectories into multi-band raster images with trip related features, and infers roads with high precision. Experiments on public data show that Ring-Net can be used to improve the completeness of a road network. Our approach is promising to bring us one step closer to fully automated map updates.","PeriodicalId":152788,"journal":{"name":"Proceedings of the 10th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132329219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Proceedings of the 10th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data 第10届ACM SIGSPATIAL大地理空间数据分析国际研讨会论文集

Proceedings of the 10th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data Pub Date : 1900-01-01 DOI: 10.1145/3557917

引用次数: 0